Identification of a monitoring nonlinear oil damper using particle filtering approach

Abstract

Oil dampers have been used in recent years for passive structural control and shock mitigation in dynamic structural systems. However, machining technical and economic problems limit its application for new and existing buildings due to the necessary precision machining of incorporating shaft bearings and pressure sealings. A kind of nonlinear oil damper that is quite different from traditional oil damper is investigated and applied to a steel building. Vibration monitoring system was instrumented on the dampers to explore their actual performance and effectivity under strong earthquakes. Based on monitoring response of the nonlinear dampers under various excitations, Bayesian model selection is employed to analyze the most probable model class which can capture main dynamic characteristics of the nonlinear oil dampers and can also be used for predicting future response as well as reliability. Then, a particle filtering approach is proposed to identify the nonlinear model of the damper and quantify the model uncertainty. The developed particle filter is capable of re-parameterizing joint posterior distribution of states and parameters of the nonlinear oil damper without augmented state estimation, which combined with Markov chain Monte Carlo algorithm so as to be able to sample high-dimensional posterior distribution. The identified models and posterior distributions of parameters show that the developed particle filter approach can be appropriately used for nonlinear parameter identification without stuck to special particles. Furthermore, the dynamic properties of the nonlinear oil damper with respect to various excitations involving different spectral characteristics are discussed.